This paper focuses on the various arraysignalprocessing methods that have been inspired by biological models. It discusses the long-standing algorithmic challenges and combinatorial complexity as well as the knowled...
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This paper focuses on the various arraysignalprocessing methods that have been inspired by biological models. It discusses the long-standing algorithmic challenges and combinatorial complexity as well as the knowledge instinct and dynamic logic of these biologically-inspired methods. It also provides examples of these methods used in GMTI tracking, slow-moving targets in SAR and UAV swarm navigation and fusion
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
Presents the introductory welcome message from the conference proceedings. May include the conference officers' congratulations to all involved with the conference event and publication of the proceedings record.
This paper presents a novel synthetic aperture method, which can be used to reduce the average sidelobe level of a random spherical volumetric array. The technique exploits a general property of random arrays in which...
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This paper presents a novel synthetic aperture method, which can be used to reduce the average sidelobe level of a random spherical volumetric array. The technique exploits a general property of random arrays in which the average sidelobe level is inversely proportional to the number of elements present
This paper presents a powerpoint presentation that focuses on the distributed signalprocessing capability of bandwidth constrained sensor networks. A simple (isotropic) universal decentralised estimation scheme (DES)...
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This paper presents a powerpoint presentation that focuses on the distributed signalprocessing capability of bandwidth constrained sensor networks. A simple (isotropic) universal decentralised estimation scheme (DES) with known PDF are presented for the ad hoc sensor network along with sensor networks with a fusion center. The paper also examines the trade-off of network size and MSE under the bandwidth constraint
作者：
Y.I. AbramovichM.D.E. TurleyN.K. SpencerIntelligence
Surveillance & Reconnaissance Division ISRD 200 Laboratories Defence Science and Technology Organisation Edinburgh SA Australia C/-ISRD
200 Laboratories DSTO Cooperative Research Centre for Sensor Signal and Information Processing Edinburgh SA Australia
For a set of T independent N-variate Gaussian training samples (T < N), we derive a test for discriminating between stationary autoregressive models of order m, AR(m), and time-varying autoregressive models of orde...
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For a set of T independent N-variate Gaussian training samples (T < N), we derive a test for discriminating between stationary autoregressive models of order m, AR(m), and time-varying autoregressive models of order m, TVAR(m)
Conventional MUSIC (multiple signal classification) method, which is a well-known super-resolution technique that employs a monostatic configuration, degrades severely when the multiple targets are deterministic becau...
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Conventional MUSIC (multiple signal classification) method, which is a well-known super-resolution technique that employs a monostatic configuration, degrades severely when the multiple targets are deterministic because the rank of the covariance matrix collapses to the value of one regardless of the number of targets. FRM (frequency response matrix), with its multistatic configuration, is also a super-resolution technique and can complement MUSIC in the target randomness spectrum. This paper demonstrates that conventional MUSIC performs better for targets with strongly random (complex) amplitudes and FRM performs better for target with weakly random amplitudes (best for deterministic amplitudes)
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